Triple

T7108021
Position Surface form Disambiguated ID Type / Status
Subject University of La Laguna E165637 entity
Predicate shortName P43 FINISHED
Object ULL
ULL is the commonly used abbreviation for the University of La Laguna, a public higher education institution located in the Canary Islands, Spain.
 E642567 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: ULL | Statement: [University of La Laguna, shortName, ULL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ULL
Context triple: [University of La Laguna, shortName, ULL]
  • A. UL
    UL is the vehicle registration code used on license plates for the city of Ulm in Germany.
  • B. UL
    UL is the two-letter IATA airline designator assigned to SriLankan Airlines, the flag carrier of Sri Lanka.
  • C. UL
    UL is the New York Stock Exchange ticker symbol for Unilever, a major multinational consumer goods company known for its food, personal care, and household products.
  • D. LLU
    LLU is the vehicle registration code assigned to the town of Kock in Poland.
  • E. LU
    LU is the two-letter ISO 3166-1 alpha-2 country code assigned to Luxembourg for international identification and data standards.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: ULL
Triple: [University of La Laguna, shortName, ULL]
Generated description
ULL is the commonly used abbreviation for the University of La Laguna, a public higher education institution located in the Canary Islands, Spain.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ULL
Target entity description: ULL is the commonly used abbreviation for the University of La Laguna, a public higher education institution located in the Canary Islands, Spain.
  • A. UL
    UL is the vehicle registration code used on license plates for the city of Ulm in Germany.
  • B. UL
    UL is the two-letter IATA airline designator assigned to SriLankan Airlines, the flag carrier of Sri Lanka.
  • C. UL
    UL is the New York Stock Exchange ticker symbol for Unilever, a major multinational consumer goods company known for its food, personal care, and household products.
  • D. LLU
    LLU is the vehicle registration code assigned to the town of Kock in Poland.
  • E. LU
    LU is the two-letter ISO 3166-1 alpha-2 country code assigned to Luxembourg for international identification and data standards.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69c6888120f081908f8f01b201dc4a4c completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e5bbd4e481909e0948d01c6b15f4 completed March 27, 2026, 8:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69c79cb4840881908196e447618b38b0 completed March 28, 2026, 9:17 a.m.
NEDg Description generation batch_69c79e779190819095aa5ab32c150d44 completed March 28, 2026, 9:25 a.m.
NED2 Entity disambiguation (via description) batch_69c79f0b2a6c819091a2d72942f8f8c5 completed March 28, 2026, 9:27 a.m.
Created at: March 27, 2026, 2:42 p.m.